AI Product Manager
K1X Open · verified Oct 24, 2026 At a glance
Manager-level Product Management role at K1X. Remote, United States · no salary stated · we estimate $120,000–$176,450 across the United States.
K1X has not published a salary for this role. The figure below is our estimate, not theirs.
remoteroles summary, based on the employer's posting.
What you'll do
- Build and regularly revise a six-to-twelve-month AI roadmap for extraction coverage, accuracy and platform work, reflecting capacity and dependencies
- Rank requests for document types, forms and fields, and ensure each has clear definitions and test data for validation
- Define and report accuracy for different audiences, connecting model measures to missed data, user corrections and filing readiness
- Set customer-relevant thresholds, business cases and release checks for model or vendor changes, and document the decisions
- Plan engineering capacity around filing peaks and the January tax-year release, while managing defect intake and correction signals
What you bring
- At least four years managing shipped B2B software, including two or more years owning an AI or ML product surface
- Hands-on understanding of ML evaluation, precision and recall, drift, inference costs and the product impact of model errors
- Experience shaping model or vendor build, buy or replacement decisions, including a decision that did not go well
- Strong product delivery skills across capacity planning, prioritization, roadmap sizing, release management and engineer-ready requirements
Who this fits
This remote U.S. role suits a product manager who can turn AI and engineering trade-offs into decisions for product, tax and finance teams. You’ll work with the Head of AI and other product managers, with planning shaped by filing peaks in September, October and November and a tax-year release each January.
From the employer
About This Role
K1x turns tax documents (K-1s, K-3s, state schedules, 990s, and a growing range of other filings and
statements) into filing-grade structured data for CPA firms, family offices, and institutional investors.
Accuracy and rework are the first thing our buyers ask about, and the models, prompts, and rules that
produce each extracted value are the product surface that answers them. That surface needs a
product manager fluent in how ML systems behave: how they are evaluated, where they fail, what
they cost, and when to replace a component rather than tune it. This role is the product manager for
the AI team: how its models and services are supplied to every K1x product line, partnering with the
Head of AI on delivery and with the other product managers on where those capabilities land.
What You'll Do
The AI roadmap. A rolling six-to-twelve-month roadmap for extraction coverage, accuracy, and the
platform underneath them, sized against real team capacity, sequenced with dependencies made
explicit, and re-planned when the evidence changes. You represent it in portfolio planning alongside
the other product roadmaps.
The demand queue. Intake and prioritization of new document types, forms, and fields requested by
Product, Tax Content, Sales, and Client Success. You rank the queue with a defensible framework
(RICE, WSJF, Kano, opportunity scoring, voice-of-customer synthesis), make sure each item arrives
with the definition and test data needed to validate it, and explain the ranking to those who did not get
their item first.
Accuracy as a product metric. Own how accuracy is defined and reported for each audience:
executive, customer, product, engineering. Translate model-level measures (per-field precision,
coverage, straight-through rate) into what a user experiences: what we missed, what they had to
touch, how many touches it took to reach a filing-ready result. Own the recurring accuracy report.
Product requirements for model and vendor decisions. Engineering evaluates and recommends
what serves each stage of the pipeline: a frontier model, one of our own models, or a vendor service.
You supply the product side of that decision: which accuracy, cost, and latency thresholds actually
matter to customers, the business case and budget for a change, and the acceptance criteria a
release must clear. You keep the decision record so the reasoning survives.
Capacity and the tax calendar. Filing peaks in September, October, and November drive usage; a
tax-year release lands every January. You plan engineering reserve around the peaks, own scope and
dates for the tax-year release, set the defect-intake service level with Client Success and QA, and
keep proof-of-concept work time-boxed.
The correction loop. Users correct extraction output inside our products. You partner with those
product managers and UX so corrections become a usable signal with field-level provenance, and over time a confidence-driven review experience.
Requirements
- 4+ years of product management on shipped B2B software, ideally fintech or regtech where a wrong number costs more than a slow one, with at least 2 years owning an AI/ML-powered product surface: document AI or extraction, search and ranking, LLM features, or an ML platform.
- Fluent in the mechanics: capacity planning against a real team, prioritization frameworks (RICE, WSJF, Kano, voice-of-customer synthesis), roadmap sizing, release management, and PRDs an engineer would actually read.
- Understand how ML products fail differently from software: precision and recall trade-offs, evaluation sets, drift, cost per inference, and why "the model got it wrong" is a product question first.
- Have made or shaped build, buy, or replace calls on model or vendor components, and can walk through one that went badly.
- Write clearly and run a tight meeting. Half this job is turning engineers' conviction into a decision document Product, Tax, and Finance can act on.
- Tax-domain knowledge is not required; Tax Content owns the rules and CPAs adjudicate. Curiosity is; the interesting failure modes live in the footnotes.
Benefits
- Unlimited Vacation Policy + Sick Time + Holidays
- Paid Parental Leave
- Fully Remote Opportunity
- Healthcare Benefits and 401(K)
- Growing Startup Culture
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